Investigating Movies Dataset¶

Introduction¶

We are going to investigate and analyze movies dataset. This data set containes a set of movies and some information about them like popularity of movie, the budget this movie took in its production, the revenue this movie made, the cast, and genres of these movies, and the rating this movie had with the count of voters.

We will clean the dataset and prepare it for analysis first then we will try to apply Exploratory Data Analysis to it and answer some questions about the data so we can get some useful information from it.

Finally we will try to identify coorelation between features and identify the independent variables and what variables are dependent on them so we can prepare a case for prediction and building ML model.

Research Questions¶

in this report we will investigate the data and try to find answers to these questions:

What are the empact of each factor in revenue from year 2006 to 2015?
What are the Top 10 Movies in terms of Movies count, Revenue, Budget, and Popularity and What are the empact on these movies regarding Revenue?
What are the top 10 Actors in terms of highest revenue, budget, and popularity? and what are the empact of these factors on revenue ?
What are the empact of genres in revenue and budget?
What are the empact of production companies in revenue and budget?

# Loading Necessary Packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats

%matplotlib inline

Data Wrangling¶

In this part we will load the data, show it to identify the kind of cleansing it need and apply data wrangling to it.

General Properties¶

# Loading Data
df = pd.read_csv('drive/My Drive/tmdb-movies.csv')
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10866 entries, 0 to 10865
Data columns (total 21 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   id                    10866 non-null  int64  
 1   imdb_id               10856 non-null  object 
 2   popularity            10866 non-null  float64
 3   budget                10866 non-null  int64  
 4   revenue               10866 non-null  int64  
 5   original_title        10866 non-null  object 
 6   cast                  10790 non-null  object 
 7   homepage              2936 non-null   object 
 8   director              10822 non-null  object 
 9   tagline               8042 non-null   object 
 10  keywords              9373 non-null   object 
 11  overview              10862 non-null  object 
 12  runtime               10866 non-null  int64  
 13  genres                10843 non-null  object 
 14  production_companies  9836 non-null   object 
 15  release_date          10866 non-null  object 
 16  vote_count            10866 non-null  int64  
 17  vote_average          10866 non-null  float64
 18  release_year          10866 non-null  int64  
 19  budget_adj            10866 non-null  float64
 20  revenue_adj           10866 non-null  float64
dtypes: float64(4), int64(6), object(11)
memory usage: 1.7+ MB

From the previous output we can notice that there are missing data like in cast, homepage, director, tagline, keywords, overview, genres,and production companies. We can say that some of these columns are not important to our analysis like homepage column which is the most column with missing data so we will drop it beside overview, keywords, tagline, imdp id which they will not be useful on our analysis.

Data Cleaning: Dropping Un-necessary columns¶

# drop ['imdb_id','homepage','tagline','keywords', 'overview'] columns from the dataframe
df.drop(['imdb_id','homepage','tagline','keywords', 'overview'], axis=1, inplace=True)
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10866 entries, 0 to 10865
Data columns (total 16 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   id                    10866 non-null  int64  
 1   popularity            10866 non-null  float64
 2   budget                10866 non-null  int64  
 3   revenue               10866 non-null  int64  
 4   original_title        10866 non-null  object 
 5   cast                  10790 non-null  object 
 6   director              10822 non-null  object 
 7   runtime               10866 non-null  int64  
 8   genres                10843 non-null  object 
 9   production_companies  9836 non-null   object 
 10  release_date          10866 non-null  object 
 11  vote_count            10866 non-null  int64  
 12  vote_average          10866 non-null  float64
 13  release_year          10866 non-null  int64  
 14  budget_adj            10866 non-null  float64
 15  revenue_adj           10866 non-null  float64
dtypes: float64(4), int64(6), object(6)
memory usage: 1.3+ MB

We can see now that there's missing data in multiple columns that we can use in our analysis but these columns are text columns like cast, genres and production companies that absloutely can't be replaced with any other data because that would cause false data. so we should drop the missing rows and work with the movies that there data is complete which are in total 9773 movie.

Data Cleaning: Dropping Missing Values¶

# dropping rows with null values
df.dropna(inplace=True)
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 9773 entries, 0 to 10865
Data columns (total 16 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   id                    9773 non-null   int64  
 1   popularity            9773 non-null   float64
 2   budget                9773 non-null   int64  
 3   revenue               9773 non-null   int64  
 4   original_title        9773 non-null   object 
 5   cast                  9773 non-null   object 
 6   director              9773 non-null   object 
 7   runtime               9773 non-null   int64  
 8   genres                9773 non-null   object 
 9   production_companies  9773 non-null   object 
 10  release_date          9773 non-null   object 
 11  vote_count            9773 non-null   int64  
 12  vote_average          9773 non-null   float64
 13  release_year          9773 non-null   int64  
 14  budget_adj            9773 non-null   float64
 15  revenue_adj           9773 non-null   float64
dtypes: float64(4), int64(6), object(6)
memory usage: 1.3+ MB

Now after we cleaned the missing data we should take care of the data types. We need to identify each correct data type for each column and assign it to that data type. We can see here that id and relase year are integers which are not true these data should be strings because making calculations on them won't make any sense. So we will convert both columns to strings. Also the release date is shown as object data type while it should be datetime data type so we will also convert the release date to date data type.

Data Cleaning: Assigning Correct Data Types¶

# convert id column from int to string
df['id'] = df['id'].astype(str)
# convert release year from int to string
df['release_year'] = df['release_year'].astype(str)
# convert release date from string to datetime data type
df['release_date'] = pd.to_datetime(df['release_date'])
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 9773 entries, 0 to 10865
Data columns (total 16 columns):
 #   Column                Non-Null Count  Dtype         
---  ------                --------------  -----         
 0   id                    9773 non-null   object        
 1   popularity            9773 non-null   float64       
 2   budget                9773 non-null   int64         
 3   revenue               9773 non-null   int64         
 4   original_title        9773 non-null   object        
 5   cast                  9773 non-null   object        
 6   director              9773 non-null   object        
 7   runtime               9773 non-null   int64         
 8   genres                9773 non-null   object        
 9   production_companies  9773 non-null   object        
 10  release_date          9773 non-null   datetime64[ns]
 11  vote_count            9773 non-null   int64         
 12  vote_average          9773 non-null   float64       
 13  release_year          9773 non-null   object        
 14  budget_adj            9773 non-null   float64       
 15  revenue_adj           9773 non-null   float64       
dtypes: datetime64[ns](1), float64(4), int64(4), object(7)
memory usage: 1.3+ MB

We Also here have 3 columns that contains lists of names or genres separated with '|' so we will convert them to lists.

# split each value in cast, genres, and production companies with using '|' delimeter 
# and conver the columns to lists datatype
df['cast'] = df['cast'].str.split('|')
df['genres'] = df['genres'].str.split('|')
df['production_companies'] = df['production_companies'].str.split('|')

Data Cleaning:Removing Outliers¶

Now Lets take a look about the data itself so we can ecognize outliers and data that doesn't make any sense.

# show summary statistics for the numeric values in the dataset
df.describe()

We can notice from the previous output some points that need to be took care of.

The range of the popularity doesn't make any sense we can see that 75% of the data between 0.000188 and 0.776380 which mean that its in the range of 0 to 1 but we can see that the maximum value is more than 32 which doesn't make any sense in terms of the data given.
The runtime column have minimum of 0 which of course doesn't make any sense because absloutly it's not convenient to have a movie that has 0 runtime also we can see that the maximum value is 877 minutes which is more than 14 hours and also it doesn't make any sense to have a movie that is last for that long time.

We can say now from the previous two observations that there's outliers in this dataset that need to be moved.

# removing rows with outliers from the dataset
df = df[(np.abs(stats.zscore(df[['popularity','runtime']])) < 3).all(axis=1)].reset_index(drop=True)
df.describe()

We can see also that there are some rows with 0 revenue and 0 budget which of course can be considered as missing data so we will drop rows with this values so the data can be clean and effictive in our hypothesis.

df = df[df['revenue'] != 0]
df.describe()

Now to make the dataset more clean and prepared for analysis we need to expand the dataframe to have a separate row to each value in the lists in cast, genres, and production companies.

Data Cleaning: Expanding each column of list in separate dataframe¶

# Expanding each column from the columns of type lists so we can analyze data regarding each value in the lists
# we saved the expanded dataframe in a new datafrmae for each column
df_cast_expanded = df.explode('cast')
df_genres_expanded = df.explode('genres')
df_production_companies_expanded = df.explode('production_companies')

Exploratory Data Analysis¶

Tip: Now that we've trimmed and cleaned our data, we're ready to move on to exploration. Compute statistics and create visualizations with the goal of addressing the research questions.

# Showing the size of the dataset and the range of years this dataset fall in.
print("Dataset Size: {}, From year {}, to year {}".format(len(df),df['release_year'].min(),df['release_year'].max()))

Dataset Size: 4569, From year 1960, to year 2015

Research Question (1) : What are the empact of each factor in revenue from year 2006 to 2015?¶

Number of Movies, Revenue, and Budget for Latest 10 years in the dataset from 2006 to 2015

# calculating the count of movies in each year in the last 10 years of the dataset
top_10_year_count = df[['release_year','id']].groupby('release_year').count().sort_values('release_year',ascending=False).head(10)
# calculating the mean of revenue in each year in the last 10 years of the dataset
top_10_year_revenue = df[['release_year','revenue']].groupby('release_year').mean().sort_values('release_year',ascending=False).head(10)
# calculating the mean of budget in each year in the last 10 years of the dataset
top_10_year_budget = df[['release_year','budget']].groupby('release_year').mean().sort_values('release_year',ascending=False).head(10)

We can see here that the most year among these 10 years that movies had produced on is 2011. And we can see that the Least year with Number of Movies is 2015.

# ploting a barplot visualize last 10 years in terms of count of movies for each year
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
sns.barplot(x = top_10_year_count.index ,y= top_10_year_count['id'] )
plt.ylabel("Number of Movies")
plt.xlabel("")
plt.title("Number of Movies Per Year From (2006 - 2015)")
plt.xticks(rotation=90);

Here We can see that the average reveune for each year differentiate but we can see that the best average made for revenues were in 2007 while the worst was in 2015. Also we can notice here that the revenue started to decrease after 2012 while it was kind of stable in period from 2007 to 2012.

# ploting a barplot visualize last 10 years in terms of average of revenue for each year
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
sns.barplot(x = top_10_year_revenue.index ,y= top_10_year_revenue['revenue'] )
plt.ylabel("Average Revenue")
plt.xlabel("")
plt.title("Average Revenue Per Year From (2006 - 2015)")
plt.xticks(rotation=90);

The decreasing of the budget is noticable and of course this explains how the descreasing of revenue is making sense. Of Course when Movies Makers start to decrease there movies budget the quality will not be great and in terms of this the revenue wil of course decrease too.

# ploting a barplot visualize last 10 years in terms of average of budget for each year
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
sns.barplot(x = top_10_year_budget.index ,y= top_10_year_budget['budget'] )
plt.ylabel("Average Budget")
plt.xlabel("")
plt.title("Average Budget Per Year From (2006 - 2015)")
plt.xticks(rotation=90)

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 <a list of 10 Text major ticklabel objects>)

Research Question (2) : What are the Top 10 Movies in terms of Movies count, Revenue, Budget, and Popularity and What are the empact on these movies regarding Revenue?¶

Top 10 Movies in terms of 'revenue', 'Budget', and 'Rating'

Top 10 movies in term of 'Revenue¶

We can see here that Movie wth Name Transformers : Dark of the Moon made the highest revenue in the dataset. These are the Top 10 Highest Movies in terms of Revenue and Now we will see some other attributes values for this top 10 Movies.

# ploting a barplot visualize top 10 movies in terms of revenue

top_10_movies_revenue = df.groupby('original_title').mean().sort_values('revenue',ascending=False).head(10).reset_index()
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='revenue',y='original_title', data=top_10_movies_revenue )
# setting text on bars
for index, row in  top_10_movies_revenue.iterrows():
  g.text(row.revenue+10000000,row.name, round(row.revenue,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Revenue")
plt.title("Revenue Per Movie For The top 10 Highest Movies In Term of Revenue From (1960 - 2015)");

We can see here that amount of budget is differentiate between Movies but it absloutly shows that the budget can be lower than others but it will get highest revenue and this lead us to make a hypothesis that when budget extremly increase it can cause of decreasing revenue but of course when it extremly decrease that wil also harm the revenue increasing due to bad production factor.

# ploting a barplot visualize top 10 movies in terms of revenue

top_10_movies_revenue = df.groupby('original_title').mean().sort_values('revenue',ascending=False).head(10).reset_index()
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='budget',y='original_title', data=top_10_movies_revenue )
# setting text on bars
for index, row in  top_10_movies_revenue.iterrows():
  g.text(row.budget+10000000,row.name, round(row.budget,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Budget")
plt.title("Budget Per Movie For The top 10 Highest Movies In Term of Revenue From (1960 - 2015)");

Top 10 movies in term of 'Rating¶

Here's We can see that the Highest Rating didn't get very high revenue and by this we can say the the rating isn't a facor to determine if this movie is successfully making revenues or not.

# ploting a barplot visualize top 10 movies in terms of rating
top_10_movies_rating = df.groupby('original_title').mean().sort_values('vote_average',ascending=False).head(10).reset_index()
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='revenue',y='original_title', data=top_10_movies_rating )
# setting text on bars
for index, row in  top_10_movies_rating.iterrows():
  g.text(row.revenue+29999999,row.name,  'Revenue = '+str(round(row.revenue,2))+', Rating = '+str(round(row.vote_average,2)), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Revenue")
plt.title("Revenue Per Movie For The top 10 Highest Movies In Term of Rating From (1960 - 2015)");

From our finding here we can see that Rating not factor that can determine with the revenuse since the top 10 rated movies differentiate in the revenue some of them are getting high revenue but some others are not.

Top 10 movies in term of 'Budget'¶

This visualization shows that increasing and decreasing budget must be controled so it can get the best revenue. To make a movie successfully be top in revenue you must not extremly decrease or extremly increase the budget. The movie must be had enough budget that make it well produced but to increase the revenue you must not to extremly increase the budget either.

# ploting a barplot visualize top 10 movies in terms of budget


top_10_movies_budget = df.groupby('original_title').mean().sort_values('budget',ascending=False).head(10).reset_index()
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (24, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='revenue',y='original_title', data=top_10_movies_budget )
# setting text on bars
for index, row in  top_10_movies_budget.iterrows():
  g.text(row.revenue+199990000,row.name, 'Revenue = '+str(round(row.revenue,2))+', Budget = '+str(round(row.budget,2)), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Revenue")
plt.title("Revenue Per Movie For The top 10 Highest Movies In Term of Budget From (1960 - 2015)");

Top 10 movies in term of 'Popularity'¶

Here we can see that pipularity is good for increasing revenue but it's not a factor to determine whether the movie is getting successful revenue or not.

# ploting a barplot visualize top 10 movies in terms of popularity

top_10_movies_popularity = df.groupby('original_title').mean().sort_values('popularity',ascending=False).head(10).reset_index()
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (24, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='revenue',y='original_title', data=top_10_movies_popularity )
# setting text on bars
for index, row in  top_10_movies_popularity.iterrows():
  g.text(row.revenue+199990000,row.name, 'Revenue = '+str(round(row.revenue,2))+', Popularity = '+str(round(row.popularity,2)), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Revenue")
plt.title("Revenue Per Movie For The top 10 Highest Movies In Term of Popularity From (1960 - 2015)");

The Empact of budget on revenue in the top 10 movies in terms of 'Budget'¶

We can see from the previous plot that the revenue decreases with increasing of the budget. And this is make us notice that there's a relation ship between revenue and budget in reverse.

# ploting revenue againest budget for the top 10 movies with highest budget
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (24, 10)})
plt.plot(df.sort_values('budget',ascending=False).head(10).sort_values('revenue',ascending=False)['original_title'],df.sort_values('budget',ascending=False).head(10).sort_values('revenue',ascending=False)['budget'])
plt.plot(df.sort_values('budget',ascending=False).head(10).sort_values('revenue',ascending=False)['original_title'],df.sort_values('budget',ascending=False).head(10).sort_values('revenue',ascending=False)['revenue'])
plt.legend(['Budget','Revenue'])
plt.xticks(rotation=90)
plt.ylabel("Revenue/Budget")
plt.xlabel("")
plt.title("Revenue Againest Budget for The top 10 Highest Movies In Term of Rating From (1960 - 2015)")
plt.show();

Research Question (3) : What are the top 10 Actors in terms of highest revenue, budget, and popularity? and what are the empact of these factors on revenue ?¶

Top 10 Actors/Actress Had Number of Movies in the dataset¶

preparing data¶

# getting the top 10 actors with highest number of movies associated with the dataset
cast_top_10_count = df_cast_expanded[['cast','id']].groupby('cast').count().sort_values('id',ascending=False).head(10)
cast_top_10_count

# calculating the mean of the revenue, budget, popularity and, Rating for the top 10 actors in terms of count of movies
top_10_cast_movie_count = df_cast_expanded[df_cast_expanded['cast'].isin(cast_top_10_count.index)]
top_10_cast_movie_count = top_10_cast_movie_count.groupby('cast').mean()
top_10_cast_movie_count

top_10_cast_movie_count.reset_index(inplace=True) # reseting the index of the dataframe

Average Revenue of top 10 Actors in terms of count of movies¶

Here we can see the List of Top 10 Actors/Actresses That Filmed The most number of Movies from 1960 to 2015 and there average revenue.

# the average revenue for top 10 actors in terms of movie counts
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='revenue',y='cast', data=top_10_cast_movie_count)
# setting text on bars
for index, row in top_10_cast_movie_count.iterrows():
    g.text(row.revenue,row.name, round(row.revenue,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Revenue")
plt.title("Average Revenue Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Number of Movies From (1960 - 2015)");

Average Budget of top 10 Actors in terms of count of movies¶

We can see that the budget of those Actors are high and it make sense. This actors are very popular they film alot of movies yearly and of course there movies will have high budget.

# the average budget for top 10 actors in terms of movie counts
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='budget',y='cast', data=top_10_cast_movie_count)
# setting text on bars
for index, row in top_10_cast_movie_count.iterrows():
    g.text(row.budget,row.name, round(row.budget,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Budget")
plt.title("Average Budget Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Number of Movies From (1960 - 2015)");

Average Popularity of top 10 Actors in terms of count of movies¶

We can see here that there popularity is high and this is make sense too.

# the average popularity for top 10 actors in terms of movie counts
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='popularity',y='cast', data=top_10_cast_movie_count)
# setting text on bars
for index, row in top_10_cast_movie_count.iterrows():
    g.text(row.popularity,row.name, round(row.popularity,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Popularity")
plt.title("Average Popularity Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Number of Movies From (1960 - 2015)");

Average Rating of top 10 Actors in terms of count of movies¶

We can see here that there Movies Average Rating is high.

# the average rating for top 10 actors in terms of movie counts
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='vote_average',y='cast', data=top_10_cast_movie_count)
# setting text on bars
for index, row in top_10_cast_movie_count.iterrows():
    g.text(row.vote_average,row.name, round(row.vote_average,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Rating")
plt.title("Average Rating Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Number of Movies From (1960 - 2015)");

Top 10 Actors/Actress Had Most Revenue since 1960 to 2015¶

preparing data¶

We can see here the most 10 actors that there movies got the highest revenue.

# grouping data by each actor/actress
cast_top_10_revenue = df_cast_expanded[['cast','revenue']].groupby('cast').sum().sort_values('revenue',ascending=False).head(10)
cast_top_10_revenue

# calculating the mean of the revenue, budget, popularity and, Rating for the top 10 actors in terms of highest revenues
top_10_cast_movie_revenue = df_cast_expanded[df_cast_expanded['cast'].isin(cast_top_10_revenue.index)]
top_10_cast_movie_revenue = top_10_cast_movie_revenue.groupby('cast').mean()
top_10_cast_movie_revenue

top_10_cast_movie_revenue.sort_values('revenue',ascending=False,inplace=True)
top_10_cast_movie_revenue.reset_index(inplace=True)

Average Revenue of top 10 Actors in terms of highest revenue¶

In this Plot we visualize the Average revenue for The top 10 Highest Actors in Terms of Revenue

# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='revenue',y='cast', data=top_10_cast_movie_revenue)
# setting text on bars
for index, row in top_10_cast_movie_revenue.iterrows():
    g.text(row.revenue,row.name, round(row.revenue,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Revenue")
plt.title("Average Revenue Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Revenue From (1960 - 2015)");

Average budget of top 10 Actors in terms of highest revenue¶

In this Plot we visualize the Average Budget for The top 10 Highest Actors in Terms of Revenue

# Average budget of top 10 Actors in terms of highest revenue
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='budget',y='cast', data=top_10_cast_movie_revenue)
# setting text on bars
for index, row in top_10_cast_movie_revenue.iterrows():
    g.text(row.budget,row.name, round(row.budget,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Budge")
plt.title("Average Budget Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Revenue From (1960 - 2015)");

Average popularity of top 10 Actors in terms of highest revenue¶

In this Plot we visualize the Average Popularity for The top 10 Highest Actors in Terms of Revenue

# Average popularity of top 10 Actors in terms of highest revenue
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='popularity',y='cast', data=top_10_cast_movie_revenue)
# setting text on bars
for index, row in top_10_cast_movie_revenue.iterrows():
    g.text(row.popularity,row.name, round(row.popularity,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Popularity")
plt.title("Average Popularity Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Revenue From (1960 - 2015)");

Average Rating of top 10 Actors in terms of highest revenue¶

In this Plot we visualize the Average Rating for The top 10 Highest Actors in Terms of Revenue

# Average Rating of top 10 Actors in terms of highest revenue
# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='vote_average',y='cast', data=top_10_cast_movie_revenue)
# setting text on bars
for index, row in top_10_cast_movie_revenue.iterrows():
    g.text(row.vote_average,row.name, round(row.vote_average,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Rating")
plt.title("Average Rating Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Revenue From (1960 - 2015)");

Top 10 Actors/Actress Had Most Budget since 1960 to 2015¶

preparing data¶

# grouping dataframe with each actor/actress in terms of highest budget
cast_top_10_budget  = df_cast_expanded[['cast','budget']].groupby('cast').sum().sort_values('budget',ascending=False).head(10)
cast_top_10_budget

top_10_cast_movie_budget = df_cast_expanded[df_cast_expanded['cast'].isin(cast_top_10_budget.index)]
top_10_cast_movie_budget = top_10_cast_movie_budget.groupby('cast').mean()
top_10_cast_movie_budget

top_10_cast_movie_budget.reset_index(inplace=True)

Average Revenue of top 10 Actors in terms of highest Budget¶

In this Plot we visualize the Average revenue for The top 10 Highest Actors in Terms of Budget

# plot properties
sns.set_style('whitegrid') 
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='revenue',y='cast', data=top_10_cast_movie_budget)
# setting text on bars
for index, row in top_10_cast_movie_budget.iterrows():
    g.text(row.revenue,row.name, round(row.revenue,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Revenue")
plt.title("Average Revenue Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Budget From (1960 - 2015)");

Average Budget of top 10 Actors in terms of highest Budget¶

In this Plot we visualize the Average Budget for The top 10 Highest Actors in Terms of Budget.

# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='budget',y='cast', data=top_10_cast_movie_budget)
# setting text on bars
for index, row in top_10_cast_movie_budget.iterrows():
    g.text(row.budget,row.name, round(row.budget,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Budget")
plt.title("Average Budget Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Budget From (1960 - 2015)");

Average Rating of top 10 Actors in terms of highest Budget¶

In this Plot we visualize the Average Budget for The top 10 Highest Actors in Terms of Budget.

# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='vote_average',y='cast', data=top_10_cast_movie_budget)
# setting text on bars
for index, row in top_10_cast_movie_budget.iterrows():
    g.text(row.vote_average,row.name, round(row.vote_average,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Rating")
plt.title("Average Rating Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Budget From (1960 - 2015)");

Average Popularity of top 10 Actors in terms of highest Budget¶

In this Plot we visualize the Average Budget for The top 10 Highest Actors in Terms of Budget.

# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='popularity',y='cast', data=top_10_cast_movie_budget)
# setting text on bars
for index, row in top_10_cast_movie_budget.iterrows():
    g.text(row.popularity,row.name, round(row.popularity,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Popularity")
plt.title("Average Popularity Per Actor/Actress's Movies For The top 10 Highest Cast In Term of Budget From (1960 - 2015)");

Research Question (4) : What are the empact of genres in revenue and budget?¶

Exploring Most Genres in Revenue, Popularity, and Budget¶

preparing data¶

# grouping dataframe with each genre and calculate the mean for each numeric column in the group
genres = df_genres_expanded.groupby('genres').mean().sort_values('popularity',ascending=False)
genres

genres.reset_index(inplace=True)

Average Popularity of Movies in Each Genre¶

We can see that the most popular genre is Animation while the least popular genre is Foreign.

# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='popularity',y='genres', data=genres)
# setting text on bars
for index, row in genres.iterrows():
    g.text(row.popularity,row.name+0.3, round(row.popularity,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Popularty")
plt.title("Average Popularity For Movies Per Genre From (1960 - 2015)");

We can see here that the most 5 popular genres are Animation, Adventures, Fantasy, Action and Science Fiction

Average Budget of Movies in Each Genre¶

genres = genres.sort_values('budget',ascending=False).reset_index(drop=True)
genres

We can see that the highest budget average made for Animation Genre while the Lowest also Made for Foriegn Genre

# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='budget',y='genres', data=genres)
# setting text on bars
for index, row in genres.iterrows():
    g.text(row.budget+2000500,row.name, round(row.budget,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Budget")
plt.title("Average Budget For Movies Per Genre From (1960 - 2015)");

Here we can see that the most 5 genres that take highest budget are Adventures, Fantasy, Animation, Action, and Family

Average Revenue of Movies in Each Genre¶

genres = genres.sort_values('revenue',ascending=False).reset_index(drop=True)
genres

We can see that the highest revenue average got for Animation Genre while the Lowest also Made for Foriegn Genre

# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='revenue',y='genres', data=genres)
# setting text on bars
for index, row in genres.iterrows():
    g.text(row.revenue+10000000,row.name, round(row.revenue,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Revenue")
plt.title("Average Revenue For Movies Per Genre From (1960 - 2015)");

From these plots we can see that Genres are a good factor to determine revenue with budget and popularity together.

And Also we can see that the same top 5 genres take highest budget are getting highest revenue.

Research Question (5) : What are the empact of production companies in revenue and budget?¶

Exploring top production companies in term of revenue and budget¶

preparing data¶

# grouping dataframe with production companies in terms of choosing highest 10 budget production companies
prod_comp_budget = df_production_companies_expanded[['production_companies','budget']].groupby('production_companies').mean().sort_values('budget',ascending=False).head(10)
prod_comp_budget

production_companies_budget = df_production_companies_expanded[df_production_companies_expanded['production_companies'].isin(prod_comp_budget.index)].groupby('production_companies').mean().reset_index()
production_companies_budget

Average Budget for top 10 Production Companies in terms of Budget¶

production_companies_budget = production_companies_budget.sort_values('budget' ,ascending=False).reset_index(drop=True) # sort dataframe based on budget

In this plot we visualize the Average Budget for movies made by specific production company. This is regarding the top 10 production companies in terms of budget.

# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='budget',y='production_companies', data=production_companies_budget)
# setting text on bars
for index, row in production_companies_budget.iterrows():
    g.text(row.budget+30000000,row.name, round(row.budget,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Budget")
plt.title("Average Budget Per Production Company for Top 10 Production Companies in terms of Budget From (1960 - 2015)");

Average Revenue for top 10 Production Companies in terms of Budget¶

production_companies_budget = production_companies_budget.sort_values('revenue' ,ascending=False).reset_index(drop=True) # sort data based on revenue

In this plot we visualize the Average Revenue for movies made by specific production company. This is regarding the top 10 production companies in terms of budget.

# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='revenue',y='production_companies', data=production_companies_budget)
# setting text on bars
for index, row in production_companies_budget.iterrows():
    g.text(row.revenue+100000000,row.name, round(row.revenue,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Revenue")
plt.title("Average Revenue Per Production Company for Top 10 Production Companies in terms of Budget From (1960 - 2015)");

Average Revenue for top 10 Production Companies in terms of Revenue¶

# Choose the highest 10 production companies revenue
prod_comp_revenue = df_production_companies_expanded[['production_companies','revenue']].groupby('production_companies').mean().sort_values('revenue',ascending=False).head(10)
prod_comp_revenue

production_companies_revenue = df_production_companies_expanded[df_production_companies_expanded['production_companies'].isin(prod_comp_revenue.index)].groupby('production_companies').mean().reset_index()
production_companies_revenue

production_companies_revenue = production_companies_revenue.sort_values('revenue',ascending=False).reset_index(drop=True) # sort data based on revenue

In this plot we visualize the Average Revenue for movies made by specific production company. This is regarding the top 10 production companies in terms of Revenue.

# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='revenue',y='production_companies', data=production_companies_revenue)
# setting text on bars
for index, row in production_companies_revenue.iterrows():
    g.text(row.revenue+10000000,row.name, round(row.revenue,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Revenue")
plt.title("Average Revenue Per Production Company for Top 10 Production Companies in terms of Revenue From (1960 - 2015)");

Average Budget for top 10 Production Companies in terms of Revenue¶

production_companies_revenue = production_companies_revenue.sort_values('budget',ascending=False).reset_index(drop=True) # sort data based on budget

In this plot we visualize the Average Budget for movies made by specific production company. This is regarding the top 10 production companies in terms of Revenue.

# plot properties
sns.set_style('whitegrid')
sns.set_context({"figure.figsize": (20, 10)})
sns.set(font_scale=2) # scale of font size of the plot
g = sns.barplot(x='budget',y='production_companies', data=production_companies_revenue)
# setting text on bars
for index, row in production_companies_revenue.iterrows():
    g.text(row.budget+10000000,row.name, round(row.budget,2), color='black', ha="center")
plt.ylabel("")
plt.xlabel("Average Budget")
plt.title("Average Budget Per Production Company for Top 10 Production Companies in terms of Revenue From (1960 - 2015)");

Exploring Coorelation Between Columns¶

g = sns.PairGrid(df, vars=["popularity", "budget",'revenue','runtime','vote_average'])
g.map(plt.scatter);

plt.plot(df.sort_values('vote_average')['vote_average'],df.sort_values('vote_average')['popularity'],linewidth=3)
plt.xlabel('Rating')
plt.ylabel('Popularity')
plt.title("Relationship between Rating and Poplarity For All Movies in the Dataset");

In this plot we can see that with increasing of the vote increasing in the popularity occur which maks a good relationship and of course make sense.

And Also from the Matrix plot we can see that the relationship between budget and revenue is high beside of course voting andf popularity.

Also Runtime is a great factor when analyzing Movies since some hypothesis can be proved like that when runtime increase of course the budget increases too. An exciting finiding here is shown related to the runtime that when it increases also revenue increase which is strange but it can be true.

Limitations¶

What was challenging while working in this dataset was :

in the Cleaning step identifying outliers and removing them also beside dealing with missing data. It was very chellenging taking the step of dropping missing data or filling it but the case we had here can't fill the missing data with any alternative so the best decision we see is to drop them so the analysis can be more clear and convenient.
Finding right questions was really challenging for me since there are a lot of questions that can be asked regarding this dataset but the important thing is to ask the right question that can support your analysis specially that the key hypothesis I chose to investigate here the relation between budget and revenue. Also finding other factors that effect the revenue.
Chosing the right visualizations was very challenging and took time to finally find the best viualizations that can prove my findings.

Conclusions¶

Tip: Finally, We can Now finalize our fnding here in some points:

The Popular movies are close to be high rated than others and this we proved it with visualization.
In The latest 5 years that are shown in the dataset the movie making industry had lower budget in movies which absloutly leads to decreasing in the revenue and that was very obvious from the visulaizations.
We fount that the top 5 generes that take higher budegt also give higher revenue.
We Also Explored the dataset in terms of cast and we found that most of the actors that there movies had hgh budget they also have higher revienue.
From the ineteresting things as we discussed earlier the runtime factor and its affects on the growth of revenue and budget.
Finally we can conclude from all of this that this dataset can be very useful to indicates the factors that can affect the movie industry like time, runtime, budget, and actors popularity.
This Analysis report is the a start for exploring more about this case and of course there will be future work like using ML to predict the revenue of future published movies in term of past movies data and asloutly getting more data from this case will be more useful for our case study here.

	popularity	budget	revenue	runtime	vote_count	vote_average	budget_adj	revenue_adj
count	9773.000000	9.773000e+03	9.773000e+03	9773.000000	9773.000000	9773.000000	9.773000e+03	9.773000e+03
mean	0.694711	1.618108e+07	4.422678e+07	102.925509	239.298782	5.963430	1.941707e+07	5.704735e+07
std	1.036879	3.220939e+07	1.225834e+08	27.876224	602.982068	0.913179	3.566467e+07	1.514432e+08
min	0.000188	0.000000e+00	0.000000e+00	0.000000	10.000000	1.500000	0.000000e+00	0.000000e+00
25%	0.232756	0.000000e+00	0.000000e+00	90.000000	18.000000	5.400000	0.000000e+00	0.000000e+00
50%	0.419765	2.000000e+05	0.000000e+00	100.000000	46.000000	6.000000	3.114254e+05	0.000000e+00
75%	0.776380	1.940000e+07	3.104204e+07	112.000000	173.000000	6.600000	2.469335e+07	4.311344e+07
max	32.985763	4.250000e+08	2.781506e+09	877.000000	9767.000000	8.700000	4.250000e+08	2.827124e+09

	popularity	budget	revenue	runtime	vote_count	vote_average	budget_adj	revenue_adj
count	9435.000000	9.435000e+03	9.435000e+03	9435.000000	9435.000000	9435.000000	9.435000e+03	9.435000e+03
mean	0.607674	1.487209e+07	3.673472e+07	102.349974	190.021410	5.928988	1.809623e+07	4.886587e+07
std	0.595210	2.876819e+07	9.123045e+07	18.560144	404.062247	0.897569	3.275766e+07	1.215011e+08
min	0.000188	0.000000e+00	0.000000e+00	21.000000	10.000000	1.500000	0.000000e+00	0.000000e+00
25%	0.231405	0.000000e+00	0.000000e+00	91.000000	18.000000	5.400000	0.000000e+00	0.000000e+00
50%	0.413991	1.140000e+05	0.000000e+00	99.000000	45.000000	6.000000	2.025573e+05	0.000000e+00
75%	0.753847	1.800000e+07	2.900911e+07	111.000000	163.000000	6.600000	2.340083e+07	4.018421e+07
max	3.791881	4.250000e+08	1.123747e+09	186.000000	7080.000000	8.700000	4.250000e+08	2.167325e+09

	popularity	budget	revenue	runtime	vote_count	vote_average	budget_adj	revenue_adj
count	4569.000000	4.569000e+03	4.569000e+03	4569.000000	4569.000000	4569.000000	4.569000e+03	4.569000e+03
mean	0.885166	2.749910e+07	7.585733e+07	106.857956	345.071788	6.114029	3.317203e+07	1.009082e+08
std	0.705516	3.612246e+07	1.192500e+08	17.107653	533.920007	0.778185	4.017637e+07	1.588566e+08
min	0.001783	0.000000e+00	2.000000e+00	26.000000	10.000000	2.100000	0.000000e+00	2.370705e+00
25%	0.390240	1.700000e+06	7.985929e+06	95.000000	48.000000	5.600000	2.370705e+06	1.088074e+07
50%	0.669161	1.500000e+07	3.085781e+07	104.000000	144.000000	6.200000	2.025573e+07	4.294259e+07
75%	1.146492	3.800000e+07	9.292120e+07	116.000000	395.000000	6.700000	4.754611e+07	1.213115e+08
max	3.791881	4.250000e+08	1.123747e+09	186.000000	7080.000000	8.400000	4.250000e+08	2.167325e+09

	id
cast
Robert De Niro	58
Bruce Willis	47
Nicolas Cage	46
Samuel L. Jackson	43
Morgan Freeman	38
Sylvester Stallone	37
Robin Williams	36
Matt Damon	36
Tom Hanks	34
Dennis Quaid	34

	popularity	budget	revenue	runtime	vote_count	vote_average	budget_adj	revenue_adj
cast
Bruce Willis	1.260711	5.178606e+07	1.351535e+08	108.574468	781.042553	5.980851	6.309823e+07	1.689008e+08
Dennis Quaid	0.696148	3.058529e+07	6.141666e+07	108.529412	219.794118	5.967647	3.555510e+07	7.570387e+07
Matt Damon	1.310826	5.135278e+07	1.262355e+08	119.500000	762.555556	6.422222	5.702723e+07	1.403463e+08
Morgan Freeman	0.876726	3.219737e+07	8.071144e+07	110.973684	398.026316	6.344737	3.748886e+07	1.020186e+08
Nicolas Cage	1.120262	4.889826e+07	1.057911e+08	111.347826	475.130435	5.926087	5.665816e+07	1.243623e+08
Robert De Niro	1.112805	3.538276e+07	8.401938e+07	115.603448	420.500000	6.289655	4.462608e+07	1.070469e+08
Robin Williams	0.990692	3.890000e+07	1.372131e+08	108.472222	432.750000	6.136111	4.747747e+07	1.791707e+08
Samuel L. Jackson	1.076694	4.575965e+07	1.196326e+08	109.906977	492.209302	6.037209	5.391620e+07	1.415165e+08
Sylvester Stallone	1.266380	4.328919e+07	1.211729e+08	105.891892	477.918919	5.975676	5.747019e+07	1.902320e+08
Tom Hanks	1.451293	5.311765e+07	2.086364e+08	119.588235	869.705882	6.573529	6.328429e+07	2.583690e+08

	revenue
cast
Tom Cruise	7424944416
Tom Hanks	7093638477
Cameron Diaz	6872169506
Eddie Murphy	6419006702
Bruce Willis	6352216193
Ben Stiller	5890610707
Will Smith	5883573851
Brad Pitt	5481007961
Angelina Jolie	5300902681
Samuel L. Jackson	5144202422

	popularity	budget	revenue	runtime	vote_count	vote_average	budget_adj	revenue_adj
cast
Angelina Jolie	1.401790	7.473077e+07	2.038809e+08	112.653846	757.269231	6.115385	8.203331e+07	2.164266e+08
Ben Stiller	1.307672	5.731481e+07	2.181708e+08	102.555556	700.962963	6.085185	6.090809e+07	2.371789e+08
Brad Pitt	1.405173	6.230645e+07	1.768067e+08	125.451613	878.290323	6.564516	7.194869e+07	2.062290e+08
Bruce Willis	1.260711	5.178606e+07	1.351535e+08	108.574468	781.042553	5.980851	6.309823e+07	1.689008e+08
Cameron Diaz	1.384904	6.036207e+07	2.369714e+08	110.310345	709.241379	6.096552	6.708929e+07	2.694319e+08
Eddie Murphy	0.976450	5.851765e+07	1.887943e+08	100.676471	370.058824	5.676471	7.388153e+07	2.521291e+08
Samuel L. Jackson	1.076694	4.575965e+07	1.196326e+08	109.906977	492.209302	6.037209	5.391620e+07	1.415165e+08
Tom Cruise	1.488141	6.263333e+07	2.474981e+08	124.600000	771.700000	6.356667	7.835299e+07	3.304155e+08
Tom Hanks	1.451293	5.311765e+07	2.086364e+08	119.588235	869.705882	6.573529	6.328429e+07	2.583690e+08
Will Smith	1.819410	1.021632e+08	3.096618e+08	113.789474	1457.842105	6.373684	1.153197e+08	3.494673e+08

	budget
cast
Bruce Willis	2433945000
Nicolas Cage	2249320000
Robert De Niro	2052200000
Eddie Murphy	1989600000
Samuel L. Jackson	1967665008
Angelina Jolie	1943000000
Will Smith	1941100000
Brad Pitt	1931500000
Tom Cruise	1879000000
Matt Damon	1848700000

	popularity	budget	revenue	runtime	vote_count	vote_average	budget_adj	revenue_adj
genres
Animation	1.336465	6.431156e+07	1.977484e+08	88.470320	638.123288	6.300000	6.990134e+07	2.251560e+08
Fantasy	1.198423	5.304079e+07	1.405580e+08	103.878282	563.064439	5.936993	6.182099e+07	1.695528e+08
Adventure	1.181404	5.362759e+07	1.432246e+08	109.079893	560.035952	6.021971	6.473026e+07	1.914375e+08
Science Fiction	1.103882	4.196411e+07	1.007498e+08	105.280000	554.948571	5.888190	5.166848e+07	1.335796e+08
Family	1.084457	4.909140e+07	1.466339e+08	97.245763	449.317797	6.046186	5.645594e+07	1.818938e+08
Action	1.053208	4.321638e+07	1.059565e+08	108.541451	496.565630	5.938083	5.247147e+07	1.409881e+08
War	0.954348	3.611623e+07	8.119169e+07	126.515873	393.246032	6.500794	4.891143e+07	1.197933e+08
Thriller	0.953548	3.010931e+07	7.411856e+07	107.701031	390.173785	6.007364	3.653322e+07	1.026118e+08
Mystery	0.924395	2.802228e+07	7.136079e+07	109.853535	342.694444	6.135101	3.439678e+07	9.301576e+07
Crime	0.888801	2.653360e+07	6.549164e+07	109.199732	359.269437	6.225737	3.242802e+07	8.939915e+07
History	0.840579	3.600390e+07	7.055427e+07	129.774436	341.563910	6.487218	4.614754e+07	9.750951e+07
Comedy	0.838748	2.518041e+07	7.698570e+07	101.048150	295.349383	5.989078	2.998513e+07	9.944330e+07
Romance	0.808713	2.076760e+07	6.667558e+07	108.757969	262.141677	6.146753	2.553038e+07	8.941841e+07
Drama	0.792082	2.098144e+07	5.597684e+07	112.875290	282.369031	6.333565	2.588760e+07	7.837717e+07
Horror	0.759849	1.530755e+07	4.951223e+07	98.750909	272.000000	5.685273	1.917837e+07	7.072060e+07
Music	0.713897	1.970322e+07	6.304476e+07	109.563218	202.948276	6.327011	2.694455e+07	1.042683e+08
Western	0.703008	3.935690e+07	5.952036e+07	118.050000	258.350000	6.356667	5.041959e+07	1.087172e+08
Documentary	0.289807	2.005171e+06	1.302323e+07	99.363636	65.350649	6.927273	2.195920e+06	1.445395e+07
TV Movie	0.273628	5.000000e+06	4.200000e+07	89.000000	35.000000	5.600000	6.951084e+06	5.838910e+07
Foreign	0.196278	4.452381e+06	7.852774e+06	106.095238	20.333333	6.057143	6.584700e+06	9.987553e+06

	budget
production_companies
Boram Entertainment Inc.	425000000.0
Blind Wink Productions	255000000.0
Silver Bullet Productions (II)	255000000.0
Propeler	225000000.0
Ozumi Films	225000000.0
Film 44	209000000.0
Apparatus Productions	200000000.0
The Halcyon Company	200000000.0
Farewell Productions	200000000.0
Indochina Productions	195000000.0

	production_companies	popularity	budget	revenue	runtime	vote_count	vote_average	budget_adj	revenue_adj
0	Apparatus Productions	2.604638	200000000	531865000	116	3822	6.7	1.872067e+08	4.978434e+08
1	Blind Wink Productions	1.214510	255000000	89289910	149	1607	6.0	2.386885e+08	8.357833e+07
2	Boram Entertainment Inc.	0.250540	425000000	11087569	100	74	6.4	4.250000e+08	1.108757e+07
3	Farewell Productions	2.979786	200000000	769653595	158	3533	5.6	2.032801e+08	7.822762e+08
4	Film 44	1.630455	209000000	303025485	131	1523	5.5	1.984966e+08	2.877967e+08
5	Indochina Productions	0.760503	195000000	1123746996	154	2456	6.1	1.890326e+08	1.089358e+09
6	Ozumi Films	1.628345	225000000	419651413	150	891	6.1	2.278770e+08	4.250173e+08
7	Propeler	1.628345	225000000	419651413	150	891	6.1	2.278770e+08	4.250173e+08
8	Silver Bullet Productions (II)	1.214510	255000000	89289910	149	1607	6.0	2.386885e+08	8.357833e+07
9	The Halcyon Company	3.789633	200000000	371353001	115	1670	5.8	2.032801e+08	3.774434e+08

	production_companies	popularity	budget	revenue	runtime	vote_count	vote_average	budget_adj	revenue_adj
0	Disney Enterprises	3.440519	9.400000e+07	8.646260e+08	100.000000	3692.000000	7.400000	1.114231e+08	1.024887e+09
1	Farewell Productions	2.979786	2.000000e+08	7.696536e+08	158.000000	3533.000000	5.600000	2.032801e+08	7.822762e+08
2	Government of Malta	3.655536	1.250000e+08	7.582399e+08	149.000000	1585.000000	6.400000	1.352086e+08	8.201647e+08
3	Indochina Productions	0.760503	1.950000e+08	1.123747e+09	154.000000	2456.000000	6.100000	1.890326e+08	1.089358e+09
4	Internationale Filmproduktion Richter	2.543535	5.200000e+07	6.098416e+08	108.000000	805.000000	6.200000	5.266490e+07	6.176395e+08
5	Littlestar	2.543535	5.200000e+07	6.098416e+08	108.000000	805.000000	6.200000	5.266490e+07	6.176395e+08
6	Pixar Animation Studios	2.849023	1.473636e+08	6.153195e+08	100.363636	2478.636364	6.954545	1.557392e+08	6.675534e+08
7	TSBD Canada Productions	1.888096	1.100000e+08	7.121719e+08	117.000000	1658.000000	5.800000	1.066338e+08	6.903780e+08
8	Total Entertainment	1.888096	1.100000e+08	7.121719e+08	117.000000	1658.000000	5.800000	1.066338e+08	6.903780e+08
9	Zohar International	1.888096	1.100000e+08	7.121719e+08	117.000000	1658.000000	5.800000	1.066338e+08	6.903780e+08